Dense 3D map construction for indoor search and rescue

被引：29

作者：

Ellekilde, Lars-Peter

Huang, Shoudong ^{[1
]}

Miro, Jaime Valls

Dissanayake, Gamini

机构：

[1] Univ Technol Sydney, Fac Engn, ARC Ctr Excellence Autonomous Syst, Sydney, NSW 2007, Australia

[2] Univ So Denmark, Maersk Inst, DK-5230 Odense M, Denmark

来源：

JOURNAL OF FIELD ROBOTICS | 2007年 / 24卷 / 1-2期

关键词：

D O I：

10.1002/rob.20173

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

The main contribution of this paper is a new simultaneous localization and mapping (SLAM) algorithm for building dense three-dimensional maps using information acquired from a range imager and a conventional camera, for robotic search and rescue in unstructured indoor environments. A key challenge in this scenario is that the robot moves in 6D and no odometry information is available. An extended information filter (EIF) is used to estimate the state vector containing the sequence of camera poses and some selected 3D point features in the environment. Data association is performed using a combination of scale invariant feature transformation (SIFT) feature detection and matching, random sampling consensus (RANSAC), and least square 3D point sets fitting. Experimental results are provided to demonstrate the effectiveness of the techniques developed. (c) 2007 Wiley Periodicals, Inc.

引用

页码：71 / 89

页数：19

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